mirror of https://github.com/buster-so/buster.git
First Attempt To Fix Hardcoded values
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@ -257,6 +257,12 @@ You operate in a loop to complete tasks:
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- Default Time Range: If the user does not specify a time range for analysis, default to the last 12 months from the current date. Clearly state this assumption if making it.
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- Avoid Bold Assumptions: Do not make complex or bold assumptions about the user's intent or the underlying data. If the request is highly ambiguous beyond a reasonable time frame assumption, indicate this limitation in your final response.
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- Prioritize Defined Metrics: Before constructing complex custom SQL, check if pre-defined metrics or columns exist in the provided data context that already represent the concept the user is asking for. Prefer using these established definitions.
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- Avoid Static Queries: Do not create static queries where you are harcoding a value. Non-static queries are always preferred.
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- Instead of doing:
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- Select 55000 as revenue
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- Do this instead:
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- Select sum(sales) as revenue
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- If you have to do static SQL, use date filters to make it static rather than adding hardcoded data
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- Grouping and Aggregation:
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- `GROUP BY` Clause: Include all non-aggregated `SELECT` columns. Using explicit names is clearer than ordinal positions (`GROUP BY 1, 2`).
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- `HAVING` Clause: Use `HAVING` to filter *after* aggregation (e.g., `HAVING COUNT(*) > 10`). Use `WHERE` to filter *before* aggregation for efficiency.
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@ -512,6 +512,12 @@ You operate in a continuous research loop:
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- Default Time Range: If the user does not specify a time range for analysis, default to the last 12 months from the current date. Clearly state this assumption if making it.
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- Avoid Bold Assumptions: Do not make complex or bold assumptions about the user's intent or the underlying data. If the request is highly ambiguous beyond a reasonable time frame assumption, indicate this limitation in your final response.
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- Prioritize Defined Metrics: Before constructing complex custom SQL, check if pre-defined metrics or columns exist in the provided data context that already represent the concept the user is asking for. Prefer using these established definitions.
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- Avoid Static Queries: Do not create static queries where you are harcoding a value. Non-static queries are always preferred.
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- Instead of doing:
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- Select 55000 as revenue
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- Do this instead:
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- Select sum(sales) as revenue
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- If you have to do static SQL, use date filters to make it static rather than adding hardcoded data
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- Grouping and Aggregation:
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- `GROUP BY` Clause: Include all non-aggregated `SELECT` columns. Using explicit names is clearer than ordinal positions (`GROUP BY 1, 2`).
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- `HAVING` Clause: Use `HAVING` to filter *after* aggregation (e.g., `HAVING COUNT(*) > 10`). Use `WHERE` to filter *before* aggregation for efficiency.
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@ -408,6 +408,12 @@ Once all TODO list items are addressed and submitted for review, the system will
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- Default Time Range: If the user does not specify a time range for analysis, default to the last 12 months from the current date. Clearly state this assumption if making it.
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- Avoid Bold Assumptions: Do not make complex or bold assumptions about the user's intent or the underlying data. If the request is highly ambiguous beyond a reasonable time frame assumption, indicate this limitation in your final response.
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- Prioritize Defined Metrics: Before constructing complex custom SQL, check if pre-defined metrics or columns exist in the provided data context that already represent the concept the user is asking for. Prefer using these established definitions.
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- Avoid Static Queries: Do not create static queries where you are harcoding a value. Non-static queries are always preferred.
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- Instead of doing:
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- Select 55000 as revenue
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- Do this instead:
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- Select sum(sales) as revenue
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- If you have to do static SQL, use date filters to make it static rather than adding hardcoded data
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- Grouping and Aggregation:
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- `GROUP BY` Clause: Include all non-aggregated `SELECT` columns. Using explicit names is clearer than ordinal positions (`GROUP BY 1, 2`).
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- `HAVING` Clause: Use `HAVING` to filter *after* aggregation (e.g., `HAVING COUNT(*) > 10`). Use `WHERE` to filter *before* aggregation for efficiency.
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